GC- or LC- based mass spectrometric surveys of biological samples yield hundreds of resolved peaks per chromatogram. Statistical significant differences between semi-quantitative peak intensities can be routinely assigned to the classes of study designs. Metabolite peaks are then often called ?putative biomarkers?. However, this approach falls too short to be of real biomedical use. Importantly, biomarkers must be validated in subsequent studies and confirmed to be specific for a diagnostic case. In order to accomplish such validations, the biomarker peaks must be unambiguously re-detected in subsequent studies despite potential drifts in chromatography or large alterations in metabolic profiles. This can best be performed by establishing mass spectrometric metabolome databases that are based on standardized data acquisition conditions. Secondly, valid biomarkers require a clear route to annotation and identification of novel compounds because unidentified metabolic signals can hardly be implemented in routine clinical screens. Thirdly, differential regulation of the identified metabolites needs to be mapped to biochemical and physiological background knowledge in order to become interpretable with respect to its biomedical relevance. Standardized approaches and databases are presented that are used at the UC Davis Genome Center, with examples from biomedical and plant genomic studies.